pygeoogc 0.17.1

Description:

pygeoogc 0.17.1

Package
Description
Status



PyNHD
Navigate and subset NHDPlus (MR and HR) using web services


Py3DEP
Access topographic data through National Map’s 3DEP web service


PyGeoHydro
Access NWIS, NID, WQP, eHydro, NLCD, CAMELS, and SSEBop databases


PyDaymet
Access daily, monthly, and annual climate data via Daymet


PyGridMET
Access daily climate data via GridMET


PyNLDAS2
Access hourly NLDAS-2 data via web services


HydroSignatures
A collection of tools for computing hydrological signatures


AsyncRetriever
High-level API for asynchronous requests with persistent caching


PyGeoOGC
Send queries to any ArcGIS RESTful-, WMS-, and WFS-based services


PyGeoUtils
Utilities for manipulating geospatial, (Geo)JSON, and (Geo)TIFF data





PyGeoOGC: Retrieve Data from RESTful, WMS, and WFS Services






































Features
PyGeoOGC is a part of HyRiver software stack that
is designed to aid in hydroclimate analysis through web services. This package provides
general interfaces to web services that are based on
ArcGIS RESTful,
WMS, and
WFS. Although
all these web services have limits on the number of features per request (e.g., 1000
object IDs for a RESTful request or 8 million pixels for a WMS request), PyGeoOGC, first, divides
the large requests into smaller chunks, and then returns the merged results.
Moreover, under the hood, PyGeoOGC uses
AsyncRetriever
for making requests asynchronously with persistent caching. This improves the
reliability and speed of data retrieval significantly. AsyncRetriever caches all request/response
pairs and upon making an already cached request, it will retrieve the responses from the cache
if the server’s response is unchanged.
You can control the request/response caching behavior and verbosity of the package
by setting the following environment variables:

HYRIVER_CACHE_NAME: Path to the caching SQLite database for asynchronous HTTP
requests. It defaults to ./cache/aiohttp_cache.sqlite
HYRIVER_CACHE_NAME_HTTP: Path to the caching SQLite database for HTTP requests.
It defaults to ./cache/http_cache.sqlite
HYRIVER_CACHE_EXPIRE: Expiration time for cached requests in seconds. It defaults to
one week.
HYRIVER_CACHE_DISABLE: Disable reading/writing from/to the cache. The default is false.
HYRIVER_SSL_CERT: Path to a SSL certificate file.

For example, in your code before making any requests you can do:
import os

os.environ["HYRIVER_CACHE_NAME"] = "path/to/aiohttp_cache.sqlite"
os.environ["HYRIVER_CACHE_NAME_HTTP"] = "path/to/http_cache.sqlite"
os.environ["HYRIVER_CACHE_EXPIRE"] = "3600"
os.environ["HYRIVER_CACHE_DISABLE"] = "true"
os.environ["HYRIVER_SSL_CERT"] = "path/to/cert.pem"
There is also an inventory of URLs for some of these web services in form of a class called
ServiceURL. These URLs are in four categories: ServiceURL().restful,
ServiceURL().wms, ServiceURL().wfs, and ServiceURL().http. These URLs provide you
with some examples of the services that PyGeoOGC supports. If you have success using PyGeoOGC with a web
service please consider submitting a request to be added to this URL inventory. You can get all
the URLs in the ServiceURL class by just printing it print(ServiceURL()).
PyGeoOGC has three main classes:

ArcGISRESTful: This class can be instantiated by providing the target layer URL.
For example, for getting Watershed Boundary Data we can use ServiceURL().restful.wbd.
By looking at the web service’s
website
we see that there are nine layers. For example, 1 for 2-digit HU (Region), 6 for 12-digit HU
(Subregion), and so on. We can pass the URL to the target layer directly, like this
f"{ServiceURL().restful.wbd}/6" or as a separate argument via layer.
Afterward, we request for the data in two steps. First, we need to get
the target object IDs using oids_bygeom (within a geometry), oids_byfield (specific
field IDs), or oids_bysql (any valid SQL 92 WHERE clause) class methods. Then, we can get
the target features using get_features class method. The returned response can be converted
into a geopandas.GeoDataFrame using json2geodf function from
PyGeoUtils.

WMS: Instantiation of this class requires at least 3 arguments: service URL, layer
name(s), and output format. Additionally, target CRS and the web service version can be provided.
Upon instantiation, we can use getmap_bybox method class to get the target raster data
within a bounding box. The box can be in any valid CRS and if it is different from the default
CRS, EPSG:4326, it should be passed using box_crs argument. The service response can be
converted into a xarray.Dataset using gtiff2xarray function from PyGeoUtils.
WFS: Instantiation of this class is similar to WMS. The only difference is that
only one layer name can be passed. Upon instantiation there are three ways to get the data:

getfeature_bybox: Get all the target features within a bounding box in any valid CRS.
getfeature_byid: Get all the target features based on the IDs. Note that two arguments
should be provided: featurename, and featureids. You can get a list of valid feature
names using get_validnames class method.
getfeature_byfilter: Get the data based on any valid
CQL filter.

You can convert the returned response of this function to a GeoDataFrame using json2geodf
function from PyGeoUtils package.


PyGeoOGC also includes several utilities:

streaming_download for downloading large files in parallel and in chunks, efficiently.
traverse_json for traversing a nested JSON object.
match_crs for reprojecting a geometry or bounding box to any valid CRS.

You can find some example notebooks here.
Furthermore, you can also try using PyGeoOGC without installing
it on your system by clicking on the binder badge. A Jupyter Lab
instance with the HyRiver stack pre-installed will be launched in your web browser, and you
can start coding!
Moreover, requests for additional functionalities can be submitted via
issue tracker.


Citation
If you use any of HyRiver packages in your research, we appreciate citations:
@article{Chegini_2021,
author = {Chegini, Taher and Li, Hong-Yi and Leung, L. Ruby},
doi = {10.21105/joss.03175},
journal = {Journal of Open Source Software},
month = {10},
number = {66},
pages = {1--3},
title = {{HyRiver: Hydroclimate Data Retriever}},
volume = {6},
year = {2021}
}


Installation
You can install PyGeoOGC using pip:
$ pip install pygeoogc
Alternatively, PyGeoOGC can be installed from the conda-forge repository
using Conda
or Mamba:
$ conda install -c conda-forge pygeoogc


Quick start
We can access
NHDPlus HR
via RESTful service,
National Wetlands Inventory from WMS, and
FEMA National Flood Hazard
via WFS. The output for these functions are of type requests.Response that
can be converted to GeoDataFrame or xarray.Dataset using
PyGeoUtils.
Let’s start the National Map’s NHDPlus HR web service. We can query the flowlines that are
within a geometry as follows:
from pygeoogc import ArcGISRESTful, WFS, WMS, ServiceURL
import pygeoutils as geoutils
from pynhd import NLDI

basin_geom = NLDI().get_basins("01031500").geometry[0]

hr = ArcGISRESTful(ServiceURL().restful.nhdplushr, 2, outformat="json")

resp = hr.get_features(hr.oids_bygeom(basin_geom, 4326))
flowlines = geoutils.json2geodf(resp)
Note oids_bygeom has three additional arguments: sql_clause, spatial_relation,
and distance. We can use sql_clause for passing any valid SQL WHERE clauses and
spatial_relation for specifying the target predicate such as
intersect, contain, cross, etc. The default predicate is intersect
(esriSpatialRelIntersects). Additionally, we can use distance for specifying the buffer
distance from the input geometry for getting features.
We can also submit a query based on IDs of any valid field in the database. If the measure
property is desired you can pass return_m as True to the get_features class method:
oids = hr.oids_byfield("PERMANENT_IDENTIFIER", ["103455178", "103454362", "103453218"])
resp = hr.get_features(oids, return_m=True)
flowlines = geoutils.json2geodf(resp)
Additionally, any valid SQL 92 WHERE clause can be used. For more details look
here.
For example, let’s limit our first request to only include catchments with
areas larger than 0.5 sqkm.
oids = hr.oids_bygeom(basin_geom, geo_crs=4326, sql_clause="AREASQKM > 0.5")
resp = hr.get_features(oids)
catchments = geoutils.json2geodf(resp)
A WMS-based example is shown below:
wms = WMS(
ServiceURL().wms.fws,
layers="0",
outformat="image/tiff",
crs=3857,
)
r_dict = wms.getmap_bybox(
basin_geom.bounds,
1e3,
box_crs=4326,
)
wetlands = geoutils.gtiff2xarray(r_dict, basin_geom, 4326)
Query from a WFS-based web service can be done either within a bounding box or using
any valid CQL filter.
wfs = WFS(
ServiceURL().wfs.fema,
layer="public_NFHL:Base_Flood_Elevations",
outformat="esrigeojson",
crs=4269,
)
r = wfs.getfeature_bybox(basin_geom.bounds, box_crs=4326)
flood = geoutils.json2geodf(r.json(), 4269, 4326)

layer = "wmadata:huc08"
wfs = WFS(
ServiceURL().wfs.waterdata,
layer=layer,
outformat="application/json",
version="2.0.0",
crs=4269,
)
r = wfs.getfeature_byfilter(f"huc8 LIKE '13030%'")
huc8 = geoutils.json2geodf(r.json(), 4269, 4326)





Contributing
Contributions are appreciated and very welcomed. Please read
CONTRIBUTING.rst
for instructions.